Food Security Based Marine Life Ecosystem for Polar Region Conditioning: Remote Sensing Analysis with Machine Learning Model

Document Type

Article

Publication Date

12-11-2024

School

Computing Sciences and Computer Engineering

Abstract

The study of oceanic physical characteristics like temperature, pressure, alkalinity, dissolved oxygen, and carbon content—as well as how best to evaluate these parameters—is known as marine life analysis. The goal is to enable marine life to survive and grow under the water safely. In addition to demonstrating the validity and precision of the suggested model, the sustainability of every equilibrium point is investigated. Numerical simulations of the suggested method as well as case study are examined to bolster the findings of this research in comparison to previous investigations. This research proposes a novel technique in food security analysis in marine life ecosystem for polar region based on remote sensing and machine learning model. Here, the input is collected as marine life ecosystem in the polar region and processed for noise removal and normalisation. The processed image has been segmented, and its features are extracted using watershed graph cut histogram convolutional neural network with radial reinforcement fuzzy model. Experimental analysis is carried out in terms of precision, AUC, correlation coefficient, accuracy, and recall for various marine life ecosystem datasets. The proposed technique attained accuracy 97%, precision 96%, AUC 95%, correlation coefficient 92%, and recall 90%.

Publication Title

Remote Sensing in Earth Systems Sciences

Volume

8

Issue

1

First Page

65

Last Page

73

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